package org.orange.aicodeheloper.ai;

import dev.langchain4j.mcp.McpToolProvider;
import dev.langchain4j.memory.ChatMemory;
import dev.langchain4j.memory.chat.MessageWindowChatMemory;
import dev.langchain4j.model.chat.ChatModel;
import dev.langchain4j.model.chat.StreamingChatModel;
import dev.langchain4j.rag.content.retriever.ContentRetriever;
import dev.langchain4j.service.AiServices;
import jakarta.annotation.Resource;
import org.orange.aicodeheloper.ai.tools.InterviewQuestionTool;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;

@Configuration
public class AiCodeHelperServiceFactory  {

    @Resource
    private ChatModel qwenChatModel;

    @Resource
    private ContentRetriever contentRetriever;

    @Resource
    private McpToolProvider mcpToolProvider;

    @Resource
    private StreamingChatModel qwenStreamingChatModel;

    @Bean
    public AiCodeHelperService aiCodeHelperService() {
        // 会话记忆
        ChatMemory chatMemory = MessageWindowChatMemory.withMaxMessages(10);
        // 构造AiService
        return AiServices.builder(AiCodeHelperService.class)
                .chatModel(qwenChatModel)
                .streamingChatModel(qwenStreamingChatModel) // 流式输出
                .chatMemory(chatMemory) // 会话记忆
                .chatMemoryProvider(memoryId -> MessageWindowChatMemory
                        .builder()
                        .id(memoryId)
                        .maxMessages(10)
                        .build()) // 每个会话独立存储
                .contentRetriever(contentRetriever) // RAG 检索增强生成
                .tools(new InterviewQuestionTool())
                .toolProvider(mcpToolProvider)
                .build();
//        return AiServices.create(AiCodeHelperService.class, qwenChatModel);
    }
}
